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Real Number Operations01:27

Real Number Operations

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The concept of real numbers includes all the values that can be represented on a continuous number line. The system began with basic counting values used for enumeration. It later expanded to include values that represent the absence of quantity and opposites of the counting values. When situations required expressing parts of a whole or dividing quantities evenly, values capable of representing such proportions were developed. When written using decimal notation, these values can end or repeat...
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Operational Amplifiers01:17

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The operational amplifier, often referred to as an op-amp, is a multifaceted building block of a circuit. This electronic component functions like a voltage-controlled voltage source and can also be used to create a voltage- or current-controlled current source. The design of an operational amplifier enables it to execute mathematical operations when external components like resistors and capacitors are linked to its terminals. An op-amp has the capacity to sum signals, amplify a signal,...
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Vector Operations01:20

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Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
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Operant Conditioning01:21

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Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Related Experiment Video

Updated: Feb 11, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

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Trusted Operations on Sensor Data †.

Hassaan Janjua1, Wouter Joosen2, Sam Michiels3

  • 1imec-DistriNet-KU Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium. hassaan.janjua@cs.kuleuven.be.

Sensors (Basel, Switzerland)
|April 28, 2018
PubMed
Summary

This study introduces a mobile framework for secure participatory sensing. It ensures data integrity from capture to delivery, maintaining trust with minimal performance impact.

Keywords:
Rich Execution Environment (REE)TrustZoneTrusted Execution Environment (TEE)authenticityfidelitynormal worldsecure worldtrusted application

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Area of Science:

  • Mobile Computing
  • Sensor Networks
  • Data Security

Background:

  • Participatory sensing leverages mobile devices for data collection.
  • Ensuring the integrity of sensor data from anonymous sources is crucial.
  • Preprocessing sensor data on-device while maintaining trust is a challenge.

Purpose of the Study:

  • To design and implement a framework for trusted sensor data capture and processing on mobile devices.
  • To ensure the security and integrity of sensor data throughout the chain of trust.
  • To enable arbitrary and predefined operations on trusted sensor data.

Main Methods:

  • Development of a framework for capturing trusted sensor data from internal and external mobile sensors.
  • Implementation of trusted operations for data preprocessing on the mobile device.
  • Establishment of a mechanism to maintain the chain of trust for sensor data.

Main Results:

  • The proposed framework successfully captures trusted sensor data.
  • Trusted operations on sensor data were developed and implemented.
  • The system maintains the chain of trust from data capture to delivery.
  • Evaluation confirmed security and integrity with minimal performance overhead.

Conclusions:

  • The developed framework provides a secure solution for participatory sensing data.
  • It effectively ensures data integrity and maintains the chain of trust.
  • The system offers a practical approach with low performance overhead for mobile applications.